CN117522377A - Big data analysis application system suitable for agricultural informatization - Google Patents
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Abstract
The invention provides a big data analysis application system suitable for agricultural informatization, which comprises a cloud server and a plurality of local data servers, wherein one local data server is responsible for information acquisition and preliminary processing of crops, the cloud server comprises a data analysis module and a guidance application module, the data analysis module receives sensor data from each local data server, analyzes reasons of abnormal crop growth according to the sensor data, and guides the application module to give corresponding suggestions for planting operations according to the reasons of abnormal crop growth. According to the invention, the storage and uploading of the sensor data are controlled based on the growth guiding parameters of crops, the data quantity related to processing is greatly reduced under the condition of ensuring the monitoring accuracy, and the reasons for abnormal growth of the crops are analyzed from multiple angles, so that the actual agricultural production is more comprehensively and effectively guided.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data analysis application system suitable for agricultural informatization.
Background
The agriculture is a national economy, and along with the development of information technology and data technology, agriculture informatization has become a necessary trend of agriculture development. Along with the wide application of modern information technologies such as the Internet of things, the Internet, big data, cloud computing, 5G, artificial intelligence and the like in the agricultural field, the landing of digital agriculture characterized by information, knowledge and intelligent equipment is possible and gradually realized.
The agricultural big data has the characteristics of high value, low density and complexity, and the data value of the agricultural big data is difficult to be completely mined through the traditional statistical method. How to reduce the data volume and complexity of operation while ensuring the full utilization of the agricultural big data value becomes the technical problem to be solved at present.
Disclosure of Invention
In order to solve the technical problems of huge calculation data volume and complex calculation process in the prior art, the big data analysis application system suitable for agricultural informatization provided by the invention comprises a cloud server and a plurality of local data servers, wherein the cloud server is respectively in communication connection with the plurality of local data servers, and one local data server is responsible for information acquisition and preliminary processing of a crop;
the cloud server comprises a data analysis module and a guidance application module, the data analysis module receives sensor data from each local data server, the reasons of abnormal crop growth are analyzed according to the sensor data, and the guidance application module gives corresponding suggestions for planting operation according to the reasons of abnormal crop growth.
Preferably, the working process of the local data server includes:
s11, receiving growth guiding parameters of crops;
s12, receiving sensor data related to a field where crops are planted, and storing the sensor data in a local data server;
s13, analyzing sensor data at a specific time point in a crop growth period, judging whether the average growth data of crops accords with growth guiding parameters, if so, entering S14, otherwise, entering S15;
s14, judging whether the time difference between the earliest sensor data stored currently and the latest sensor data is larger than 15 days, if so, deleting all the sensor data with the time difference larger than 15 days from the latest sensor data by taking the latest sensor data as a reference, otherwise, returning to S12;
and S15, transmitting all sensor data of the local data server to the cloud server, emptying the local data server, and returning to S12.
Preferably, in S11, the growth guide parameter includes growth data of the crop at a specific time point in the growth cycle.
Preferably, in S12, the sensor includes a camera and a humidity sensor, the camera is uniformly disposed around the field, and the humidity sensor is uniformly distributed in the middle of the field.
Preferably, in S13, the image data in the sensor data are integrated together to obtain a complete image about the field, the complete image is analyzed to obtain average growth data of the crop, and if the average growth data is within a range of values of the growth guiding parameter, or if the average growth data is smaller than a minimum value of the range of values and an absolute value of a difference between the average growth data and the minimum value is smaller than 10%, or if the average growth data is larger than a maximum value of the range of values and an absolute value of a difference between the average growth data and the maximum value is smaller than 10%, it is indicated that the average growth data of the crop meets the growth guiding parameter.
Preferably, the specific working process of the data analysis module includes:
s21, downloading a satellite remote sensing image according to the date of data analysis, and preprocessing the satellite remote sensing image;
s22, analyzing the occurrence condition of the plant diseases and insect pests according to the satellite remote sensing image;
s23, analyzing the relationship between illumination and abnormal crop growth according to the satellite remote sensing image and the sensor data;
s24, analyzing the relation between the planting condition and the abnormal growth of crops according to the satellite remote sensing image and the sensor data.
Preferably, in S21, the pretreatment process specifically includes radiation binding, atmospheric correction, geometric correction, cloud removal, and planting area identification.
Preferably, in S22, spectral features of single-phase and multi-phase vegetation indexes of the image data are extracted, spectral features of disease monitoring are screened, and pest and disease damage occurrence conditions of crops are analyzed based on the divergence of the spectral information.
Preferably, in S23, a greenness vegetation index is obtained according to a satellite remote sensing image, a second component of the greenness vegetation index represents greenness, the second component of the greenness vegetation index forms a greenness data sequence according to a time sequence, image data in sensor data is divided into a first sub-image and a second sub-image, the first sub-image contains image information related to crops, the second sub-image contains image information related to environments other than crops, fusion analysis is performed on the first sub-image photographed at the same time, illumination data at a specific time is determined, each illumination data forms an illumination intensity sequence according to a time sequence, fusion analysis is performed on the second sub-image photographed at the same time, growth data at a specific time is determined, each growth data forms a growth data sequence according to a time sequence, and correlation analysis is performed between the growth data sequence and the illumination intensity sequence to determine whether a strong correlation relationship exists between illumination and crop growth abnormality.
Preferably, in S24, a greenness vegetation index is obtained according to a satellite remote sensing image, a first component of the greenness vegetation index represents soil brightness, the first component of the greenness vegetation index forms a soil brightness data sequence according to a time sequence, image data in sensor data is divided into a first sub-image and a second sub-image, the first sub-image contains image information related to crops, the second sub-image contains image information related to environments other than crops, fusion analysis is performed on the second sub-image photographed at the same time, growth data at a specific time is determined, each growth data forms a growth data sequence according to a time sequence, humidity data in the sensor data forms a humidity data sequence according to a time sequence, and correlation analysis is performed between the soil brightness data sequence and the growth data sequence and between the growth data sequence and the humidity data sequence respectively to determine whether a strong correlation exists between watering operation and fertilizing operation and crop growth abnormality.
Compared with the prior art, the invention has the following beneficial effects:
the sensor data is controlled to be stored and uploaded based on the growth guiding parameters of crops, the data quantity related to processing is greatly reduced under the condition of ensuring the monitoring accuracy, remote sensing data and sensor data are combined, and the reasons for abnormal growth of the crops are analyzed from multiple angles, so that actual agricultural production is guided more comprehensively and effectively.
Drawings
FIG. 1 is a schematic diagram of a big data analysis application system of the present invention.
Detailed Description
For a clearer understanding of technical features, objects, and effects of the present invention, a specific embodiment of the present invention will be described with reference to the accompanying drawings.
As shown in fig. 1, the big data analysis application system suitable for agricultural informatization provided by the invention comprises a cloud server and a plurality of local data servers, wherein the cloud server is respectively in communication connection with the plurality of local data servers, and one local data server is responsible for information acquisition and preliminary treatment of crops.
The working process of the local data server comprises the following steps:
s11, receiving growth guiding parameters of crops, wherein the growth guiding parameters comprise growth data of the crops at specific time points in a growth period, such as plant height, leaf number and the like, obtaining initial growth data of the crops by summarizing related literature data, and adjusting the initial growth data according to planting test data of the crops in local climates and soil environments so as to obtain the growth guiding parameters of the crops.
And S12, receiving sensor data related to a field for planting crops, and storing the sensor data in a local data server, wherein the sensor comprises a camera and a humidity sensor, the camera is uniformly arranged around the field, and the humidity sensor is uniformly distributed in the middle of the field.
S13, analyzing the sensor data at a specific time point in the crop growth period, judging whether the average growth data of the crops accords with the growth guiding parameters, if so, entering S14, otherwise, entering S15. Specifically, the image data in the sensor data are integrated together to obtain a complete image of the field, the complete image is analyzed to obtain average growth data of the crop, and if the average growth data is within a numerical range of the growth guiding parameter, or the average growth data is smaller than the minimum value of the numerical range and the absolute value of the difference between the average growth data and the minimum value is smaller than 10%, or the average growth data is larger than the maximum value of the numerical range and the absolute value of the difference between the average growth data and the maximum value is smaller than 10%, the average growth data of the crop is in accordance with the growth guiding parameter.
And S14, judging whether the time difference between the earliest sensor data stored currently and the latest sensor data is larger than 15 days, if so, deleting all the sensor data with the time difference larger than 15 days from the latest sensor data by taking the latest sensor data as a reference, otherwise, returning to S12.
And S15, transmitting all sensor data of the local data server to the cloud server, emptying the local data server, and returning to S12.
The cloud server comprises a data analysis module and a guidance application module. The data analysis module receives the sensor data from each local data server and analyzes the reasons of abnormal crop growth according to the sensor data. The guiding application module gives corresponding suggestions for operations such as watering, fertilization and the like according to the reasons of abnormal crop growth. The specific working process of the data analysis module comprises the following steps:
s21, downloading a satellite remote sensing image according to a date of data analysis, preprocessing the satellite remote sensing image, wherein the preprocessing process specifically comprises radiation binding, atmospheric correction, geometric correction, cloud removal and planting area identification, and the planting area identification refers to the identification of a planting area of crops from the satellite remote sensing image based on pre-stored GIS coordinate information.
S22, analyzing the occurrence of plant diseases and insect pests according to the satellite remote sensing image, specifically, extracting spectral features of single-phase and multi-phase vegetation indexes of the image data, screening the spectral features of disease monitoring, and analyzing the occurrence of plant diseases and insect pests of crops based on the divergence of the spectral information.
S23, analyzing the relation between illumination and crop growth abnormality according to the satellite remote sensing image and the sensor data, specifically, acquiring a greenness vegetation index according to the satellite remote sensing image, wherein the second component of the greenness vegetation index represents greenness, forming a greenness data sequence according to time sequence by the second component of the greenness vegetation index, dividing image data in the sensor data into a first sub-image and a second sub-image, wherein the first sub-image comprises image information related to crops, the second sub-image comprises image information related to environments outside the crops, performing fusion analysis on the first sub-image shot at the same time, judging illumination data at a specific time, forming an illumination intensity sequence according to time sequence by each illumination data, performing fusion analysis on the second sub-image shot at the same time, judging growth data at the specific time, forming a growth data sequence according to time sequence by each growth data, and performing correlation analysis between the growth data sequence and the illumination intensity sequence to judge whether a strong correlation relation exists between illumination and crop growth abnormality.
S24, analyzing the relation between planting conditions and crop growth anomalies according to satellite remote sensing images and sensor data, specifically, acquiring a green vegetation index according to the satellite remote sensing images, wherein the first component of the green vegetation index represents soil brightness, forming a soil brightness data sequence according to time sequence by the first component of the green vegetation index, dividing image data in the sensor data into a first sub-image and a second sub-image, wherein the first sub-image comprises image information related to crops, the second sub-image comprises image information related to environments outside the crops, performing fusion analysis on the second sub-image shot at the same time, judging growth data at specific time, forming a growth data sequence according to time sequence by each growth data, forming a humidity data sequence according to time sequence by the humidity data in the sensor data, and performing correlation analysis between the soil brightness data sequence and the growth data sequence and the humidity data sequence respectively so as to judge whether a strong correlation exists between watering operation and fertilizing operation and crop growth anomalies.
Compared with the prior art, the invention has the following beneficial effects:
the sensor data is controlled to be stored and uploaded based on the growth guiding parameters of crops, the data quantity related to processing is greatly reduced under the condition of ensuring the monitoring accuracy, remote sensing data and sensor data are combined, and the reasons for abnormal growth of the crops are analyzed from multiple angles, so that actual agricultural production is guided more comprehensively and effectively.
The foregoing description of the preferred embodiments of the present invention should not be construed as limiting the scope of the invention. It should be noted that equivalent changes to the solution of the present invention without departing from the design structure and principle of the present invention are considered as the protection scope of the present invention for those skilled in the art.
Claims (10)
1. The big data analysis application system is characterized by comprising a cloud server and a plurality of local data servers, wherein the cloud server is respectively in communication connection with the plurality of local data servers, and one local data server is responsible for information acquisition and preliminary processing of a crop;
the cloud server comprises a data analysis module and a guidance application module, the data analysis module receives sensor data from each local data server, the reasons of abnormal crop growth are analyzed according to the sensor data, and the guidance application module gives corresponding suggestions for planting operation according to the reasons of abnormal crop growth.
2. The big data analysis application system of claim 1, wherein the local data server comprises:
s11, receiving growth guiding parameters of crops;
s12, receiving sensor data related to a field where crops are planted, and storing the sensor data in a local data server;
s13, analyzing sensor data at a specific time point in a crop growth period, judging whether the average growth data of crops accords with growth guiding parameters, if so, entering S14, otherwise, entering S15;
s14, judging whether the time difference between the earliest sensor data stored currently and the latest sensor data is larger than 15 days, if so, deleting all the sensor data with the time difference larger than 15 days from the latest sensor data by taking the latest sensor data as a reference, otherwise, returning to S12;
and S15, transmitting all sensor data of the local data server to the cloud server, emptying the local data server, and returning to S12.
3. The big data analysis application system of claim 2, wherein in S11, the growth guidance parameter includes growth data of the crop at a specific time point in the growth cycle.
4. The big data analysis application system of claim 2, wherein in S12, the sensor includes a camera and a humidity sensor, the camera is uniformly disposed around the field, and the humidity sensor is uniformly distributed in the middle of the field.
5. The big data analysis application system of claim 4, wherein in S13, the image data in the sensor data are integrated together to obtain a complete image of the field, the complete image is analyzed to obtain average growth data of the crop, and if the average growth data is within a range of values of the growth guiding parameter, or the average growth data is smaller than a minimum value of the range of values and an absolute value of a difference between the average growth data and the minimum value is smaller than 10% of the minimum value, or the average growth data is larger than a maximum value of the range of values and an absolute value of a difference between the average growth data and the maximum value is smaller than 10% of the maximum value, it is indicated that the average growth data of the crop conforms to the growth guiding parameter.
6. The big data analysis application system of claim 4, wherein the specific working process of the data analysis module comprises:
s21, downloading a satellite remote sensing image according to the date of data analysis, and preprocessing the satellite remote sensing image;
s22, analyzing the occurrence condition of the plant diseases and insect pests according to the satellite remote sensing image;
s23, analyzing the relationship between illumination and abnormal crop growth according to the satellite remote sensing image and the sensor data;
s24, analyzing the relation between the planting condition and the abnormal growth of crops according to the satellite remote sensing image and the sensor data.
7. The big data analysis application system of claim 6, wherein the preprocessing process includes radiation binding, atmospheric correction, geometric correction, cloud removal, and planting area identification in particular in S21.
8. The big data analysis application system according to claim 6, wherein in S22, spectral features of single-phase and multi-phase vegetation indexes of the image data are extracted, spectral features of disease monitoring are screened, and pest and disease occurrence of crops is analyzed based on the divergence of the spectral information.
9. The big data analysis application system according to claim 6, wherein in S23, a greenness vegetation index is obtained according to a satellite remote sensing image, the second component of the greenness vegetation index represents greenness, the second component of the greenness vegetation index is formed into a greenness data sequence according to time sequence, image data in sensor data is divided into a first sub-image and a second sub-image, the first sub-image contains image information related to crops, the second sub-image contains image information related to environments outside the crops, fusion analysis is performed on the first sub-image photographed at the same time, illumination data at a specific time is determined, each illumination data is formed into an illumination intensity sequence according to time sequence, fusion analysis is performed on the second sub-image photographed at the same time, growth data at a specific time is determined, each growth data is formed into a growth data sequence according to time sequence, and correlation analysis is performed between the growth data sequence and the illumination intensity sequence to determine whether a strong correlation relationship exists between illumination and the growth abnormality of the crops.
10. The big data analysis application system according to claim 6, wherein in S24, a greenness vegetation index is obtained according to a satellite remote sensing image, a first component of the greenness vegetation index represents soil brightness, the first component of the greenness vegetation index is formed into a soil brightness data sequence according to time sequence, image data in sensor data is divided into a first sub-image and a second sub-image, the first sub-image contains image information related to crops, the second sub-image contains image information related to environments outside the crops, fusion analysis is performed on the second sub-image photographed at the same time, growth data at a specific time is determined, each growth data is formed into a growth data sequence according to time sequence, humidity data in the sensor data is formed into a humidity data sequence according to time sequence, and correlation analysis is performed respectively between the soil brightness data sequence and the growth data sequence and between the growth data sequence and the humidity data sequence to determine whether a strong correlation exists between a watering operation and a fertilizing operation and a crop growth abnormality.
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